Who is this presentation for?

Prerequisite knowledge

Basic knowledge of mathematics

Previous experience with neural networks or Python not required

What you'll learn

Gain an overview of implementing neural networks using TensorFlow

Description

With TensorFlow, deep machine learning has transitioned from an area of research into mainstream software engineering. Martin Görner walks you through building and training a neural network that recognizes handwritten digits with >99% accuracy using Python and TensorFlow. Along the way, Martin discusses many standard deep learning techniques such as minibatching, learning rate decay, dropout, convolutional networks, and more and demonstrates how to implement them in TensorFlow.

Martin Görner

Google

Martin Görner works in developer relations at Google, where he focuses on parallel processing and machine learning. Passionate about science, technology, coding, algorithms, and everything in between, Martin’s first role was in the Computer Architecture Group at STMicroelectronics. He also spent 11 years shaping the nascent ebook market, starting at Mobipocket, which later became the software part of the Amazon Kindle and its mobile variants. He holds a degree from Mines Paris Tech.